At HP for approximately eight months, Wahlin previously put the security in place after the infamous PlayStation breach while he was the chief security officer (CSO) at Sony Network Entertainment. Prior to that, he was the CSO at McAfee, after a stint as CSO at Los Alamos Laboratory. Years ago, Wahlin got his start doing counterintelligence for the US Army during the Cold War.

Gardner: There's been a lot of discussion about security and a lot of discussion about big data. I'm curious as to how these are actually related.

Wahlin: Big data is quite an interesting development for us in the field
of security. If we look back on how we used to do security, trying to
determine where our enemies were coming from, what their capacities
were, what their targets were, and how we're gathering intelligence to
be able to determine how best to protect the company, our resources were
quite limited.

If you're a
battlefield commander, and you're looking at how to deploy defenses, how
would you deploy those offenses, and what would be the targets that
your enemies are looking for? You typically then look at gathering
intelligence. This intelligence comes through multiple sources, whether
it's electronic or human signals, and you begin to process the
intelligence that's gathered, looking for insights into your enemy.

Moving defenses

This
could be the enemy’s capabilities, motivation, resourcing, or targets.
Then, by that analysis of that intelligence, you can go through a
process of moving your defenses, understanding where the targets may be,
and adjusting your troops on the ground.

Big
data has now given us the ability to collect more intelligence from
more sources at a much more rapid pace. As we go through this, we're
looking at understanding these types of questions that we would ask as
if we were looking at direct adversaries.

We're
looking at what these capabilities are, where people are attacking from,
why they're attacking us, and what targets they're looking for within
our company. We can gather that data much more rapidly through the use
of big data and apply these types of analytics.

We
begin to ask different questions of the data and, based on the type of
questions we're asking, we can come up with some rather interesting
information that we never could get in the past. This then takes us to a
position where that advanced analytics allows us to almost predict
where an enemy might hit.

That’s in the future, I
believe. Security is going from the use of prevention, where I'm
tackling a known bad thing, to the point where I can use big data to
analyze what's happening in real time and then predict where I may be
attacked, by whom, and at what targets. That gives me the ability to
move the defenses around in such a way that I can protect the high-value
items, based on the intelligence that I see coming in through the
analytics that we get out of big data.

Muller: Brett, you talk a lot about the idea of getting
in front of the problem. Can you talk a little bit about your point of
view on how security, from your perspective as a practitioner, has
evolved over the last 10-15 years?

Wahlin:
Certainly. That’s a great question. Years ago, we used to be about
trying to prevent the known bad from happening. The questions we would
ask would always be around, can it happen to us, and if it does, can we
respond to it? What we have to look at now is the fact that the question
should change. It should be not, "Can it happen to us," but "When is it
going to happen to us?" And not, "Can we respond to it," but "How can
we survive it?"

If we look at that type of a mind-shift
change, that takes us back to the old ways of doing security, where you
try to prevent, detect, and respond. Basically, you prevented the known
bad things from happening.

This went back to the days of -- pick your favorite attack from years ago. One that I remember is very telling. It was Code Red,
and we weren’t prepared for it. It hit us. We knew what the signature
looked like and we were able to stop it, once we identified what it was.
That whole preventive mechanism, back in the day, was pretty much what
people did for security.

Fast forward several years,
and you get into that new era of security threats highlighted by attacks
like Aurora, when it came out. Suddenly, we had the acronyms that flew
all over, such as APT -- advanced persistent threats -- and advanced malware.
Now, we have attacks that you can't prevent, because you don’t know
them. You can't see them. They're zero-days. They're undiscovered
malware that’s in your system already.

Detect and respond

That
changed the way we moved our security. We went from prevent to a big
focus on not just preventing, because that becomes a hygiene function.
Now, we move in to detect-and-respond view, where we're looking for
anomalies. We're looking for the unknown. We're beefing up the ability
to quickly respond to those when we find them.

The
evolution, as we move forward, is to add a fourth dimension to this. We
prevent, detect, respond, and predict. We use elements like big data to
understand not only how to get situational awareness, where we connect
the dots within our environment, but taking it one step further and
being able to predict where that next stop might land. As we evolve in
this particular area, getting to that point where we can understand and
predict will become a key capability that security departments must have
in future.

As I hear you
talking about getting more data, being proactive, and knowing yourself
as an organization, Brett, it
sounds quite similar to what we have been hearing for many years from
the management side, to know yourself
to be able better maintain performance standards and therefore be able
to quickly remediate when something went wrong.

Are we
seeing a confluence between good IT management practices and good security
practices, and should we still differentiate between the two?

One of the elements that we look at, of course, is how to add all this
additional complexity and additional capability into security and yet
still continue to drive value to the business and drive costs out

Wahlin:
As we move into the good management of IT, the good management of knowing yourself, there's a hygiene element that appears within the
correlation end of the security industry. One of the elements that we
look at, of course, is how to add all this additional complexity and
additional capability into security and yet still continue to drive
value to the business and drive costs out. So we look for areas of
efficiencies and again we will draw many similarities.

As
you understand the managing of your environments and knowing yourself,
we'll begin to apply known standards that we'll really use in the
governance perspective. This is where you will take your hygiene,
instead of looking at a very elaborate risk equations. You'll have your
typical "risk equals threat times vulnerability times impact," and what
are my probabilities.

Known standards

It
gets very confusing. So we're trying to cut cost out of those, saying
that there are known standards out there. Let's just use them. You can
use the ISO 27001, NIST 800-53, or even something like a PCI DSS. Pick your standard, and that then becomes the baseline of control that you want to do. This is knowing yourself.

With
these controls, you apply them based on risk to the company. Not all
controls are applied equally, nor should they be. As you apply the
control based on risk, there is evaluation assessment. Now, I have a
known baseline that I can measure myself against.

As
you began to build that known baseline, did you understand how well
you're doing from a hygiene perspective? These are all the things that
you should be doing that give you a chance to understand what your
problem areas are.

As you begin to understand those
metrics, you can understand where you might have early-warning
indicators that would tell you that that you might need to pay attention
to certain types of threats, risks, or areas within the company.

There are two types of organizations -- those that have been hacked and those that know they're being hacked.

There are a lot of similarities as you would look at the IT infrastructures, server
maintenance, and understanding of those metrics for early warnings or
early indicators of problems. We're trying to do the same security,
where we make it very repeatable. We can make it standards-based and we
can then extend that across the company, of course always being based on
risk.

Muller: There is one more element to that, Dana, such as the evolution of IT management through, say, a framework like ITIL, where you very deliberately break down the barriers between silos across IT.

Similarly,
I increasingly find with security that collaboration across
organizations -- the whole notion of general threat intelligence – forms
one of the greatest sources of potential intelligence about an imminent
threat. That can come from the operational data, or a lot of
operational logs, and then sharing that situational awareness between
the operations team is powerful.

At least this works
in the experience that I have seen with many of our clients as they
improve security outcomes through a heightened sense of what's actually
going on, across the infrastructure with customers or users.

One of the greatest challenges we have
in moving through Brett’s evolution that he described is that many
executives still have the point of view that I have a little green light
on my desktop, and that tells me I don’t have any viruses today. I can
assume that my organization is safe. That is about as sophisticated a
view of security as some executives have.

Increased awareness

Then,
of course, you have an increasing level of awareness that that is a
false sense of security, particularly in the financial services
industry, and increasingly in many governments, certainly national
government. Just because you haven't heard about a breach today, that
doesn’t mean that one isn't actually either being attempted or is, in
fact, being successful.

One of the great challenges we
have is just raising that executive awareness that a constant level of
vigilance is critical. The other place where we're slowly making
progress is that it's not necessarily a bad thing to share negative experiences.

We have to understand which ones of these we need to pay attention to
and have the ability to not only correlate amongst ourselves at the
company, but correlate across an industry.

Wahlin:
Absolutely. We look at the inevitability of the fact that networks
are penetrated, and they're penetrated on a daily basis. There's a
difference between having unwanted individuals within your network and
having the data actually exfiltrated and having a reportable breach.

As
we understand what that looks like and how the adversaries are actually
getting into our environment, that type of intelligence sharing
typically will happen amongst peers. But the need for the ability to
actually share and do so without repercussions is an interesting
concept. Most companies won't do it, because they still have that
preconceived notion that having somebody in your environment is binary
-- either my green light is on, and it's not happening, or I've got the
red light on, and I've got a problem.

In fact, there
are multiple phases of gray that are happening in there, and the ability
to share the activities, while they may not be detrimental, are
indicators that you have an issue going on and you need to be paying
attention to it, which is key when we actually start pointing
intelligence.

I've seen these logs. I've seen this type
of activity. Is that really an issue I need to pay attention to or is
that just an automated probe that’s testing our defenses? If we look at
our environment, the size of HP and how many systems we have across the
globe, you can imagine that we see that type of activity on a
second-by-second basis.

We have to understand which
ones of these we need to pay attention to and have the ability to not
only correlate amongst ourselves at the company, but correlate across an
industry.

HP may be attacked. Other high-tech
companies may also be attacked. We'll get supply-chain attacks. We look
at various types of politically motivated attacks. Why are they hitting
us? So again, it's back to the situational awareness. Knowing the
adversary and knowing their motivations, that data can be shared. Right
now, it's usually in an ad-hoc way, peer-to-peer, but definitely there's
room for some formalized information sharing.

Information sharing

Muller:
Especially when you consider the level of information sharing that goes
on in the cybercrime world. They run the equivalent of a Facebook
almost. There is a huge amount of information sharing that goes on in
that community. It's quite well structured. It's quite well organized.
It hasn’t necessarily always been that well organized on the defense
side of the equation. I think what you're saying is that there's
opportunity for improvement.

Wahlin: Yes, and as
we look at that opportunity, the counterintelligence person in me
always has to stand up and say, "Let's make sure that we're sharing it
and we understand our operational security, so that we're sharing that
in a way that we're not giving away our secrets to our adversaries." So
while there is an opportunity, we also have to be careful with how we
share it.

Muller: You, of course, wind up in the
situation where you could be amplifying bad information as well. If you
were paranoid enough, you could assume that the adversary is actually
deliberately planting some sort of distraction at one corner of the
organization in order to get to everybody focused on that, while they
quietly sneak in through the backdoor.

Wahlin: Correct.

Gardner:
Brett, returning to this notion of actionable intelligence and the role
of big data as an important tool, where do you go for the data? Is it
strictly the systems, the systems log information? Is there an operational side
to that that you tap more than the equipment, more than the behaviors?
What are the sources of data that you want to analyze in order to be
better at security?

Let's make sure that we're sharing it and we understand our operational
security, so that we're sharing that in a way that we're not giving away
our secrets to our adversaries.

Wahlin:
The sources that we use are evolving. We have our traditional sources,
and within HP, there is an internal project that is now going into
alpha. It's called Project HAVEn and that’s really a combination of ArcSight, Vertica, and Autonomy, integrating with Hadoop.
As we build that out and figure out what our capabilities are to put
all this data into a large collection and being able to ask the
questions and get actionable results out of this, we begin to then
analyze our sources.

Sources are obvious as we look at
historical operation and security perspective. We have all the log files
that are in the perimeter. We have application logs, network
infrastructure logs, such as DNS, Active Directory, and other types of LDAP logs.

Then
you begin to say, what else can we throw in here? That’s pretty much
covered in a traditional ArcSight type of an implementation. But what
happens if I start throwing things such as badge access or in-and-out
card swipes? How about phone logs? Most companies are running IP phone. They will have logs. So what if I throw that in the equation?

What if I go outside to social media and begin to throw things such as Twitter
or Facebook feeds into this equation? What if I start pulling in public
searches for government-type databases, law enforcement databases, and
start adding these? What results might I get based on all that data
commingling?

We're not quite sure at this point. We've
added many of these sources as we start to look and ask questions and
see from which areas we're able to pull the interesting correlations
amongst different types of data to give us that situational awareness.

There's
still much to be done here, much to be discovered, as we understand the
types of questions that we should be asking. As we look at this data
and the sources, we also look at how to create that actionable
intelligence.

Disparate sources

The
type of analysts that we typically use in a security operations center
are very used to ArcSight. I ingest the log and I see correlations.
They're time-line driven. Now, we begin to ask questions of multiple
types of data sources that are very disparate in their information, and
that takes a different type of analyst.

Not only do we
have different types of sources, but we have to have different types of
skill sets to ask the right questions of those sources. This will
continue to evolve. We may or may not find value as we add sources. We
don’t want to add a source just for the heck of it, but we also want to
understand that we can get very creative with the data as it comes
together.

Muller: There are actually two things that I think are important to follow up on
here. The first is that, as it's true of every type of analytics
conversation I am having today, everyone talks about the term "data scientist."
I prefer the term "data artist," because there's a certain artistry to
working out what information feeds I want to bring in.

The other element is
that, once we've got that information, one of the challenges is that we
don’t want to add to the overhead or the burden of processing that
information. So it's being able to increasing apply intelligence to, as
Brett talked about, mechanistic patterns that you can determine with
traditional security information. Event management solutions are rather mechanistic. In other words, you apply a set of logical rules to them.

When you're looking at behavioral activities, rules may not be quite as
robust as looking at techniques such as information clustering.

Increasingly,
when you're looking at behavioral activities, rules may not be quite as
robust as looking at techniques such as information clustering, where
you look for hotspots of what seem like unrelated activities at first,
but turn out later to be related.

There's a whole bunch of science in the area of crime investigation that we've applied to cybercrime,
using some of the techniques, Autonomy for example, to uncover fraud in
the financial services market. That automation behind those techniques
increasingly is being applied to the big-data problem that security is
starting to deal with.

Gardner: You were describing this opportunity to bring so
much different information together, but you also
might have unintended consequences. Have you
plumbed that at all?

Wahlin:
Yes. As we further evaluate these data sources and the ability to
understand, I believe that the insight into using the big data, not only
for security, but as more of a business intelligence (BI)
type of perspective has been well-documented. Our focus has really been
on trying to determine the patterns and characteristics of usage.

Developing patterns

While
we look at it from a purely security mindset, where we try to develop
patterns, it takes on a counter-intelligence way of understating how
people go, where people go, and what do they do. As people try to be
unique, they tend to fall into patterns that are individual and specific
to themselves. Those patterns may be over weeks or months, but they're
there.

Right now, a lot of times, we'll be asked as a
security organization to provide badge swipes as people go in and out of
buildings. Can we take that even further and begin to understand where
the efficiency would come in based on behaviors and characteristics with
workforces. Can we divide that into different business units or
geography to try to determine the best use of limited resources across
companies? This data could be used in those areas.

The
unintended consequence that you brought up, as we look at this and
begin to come up with patterns of individuals, is that it begins to
reveal a lot about how people interact with systems -- what systems they
go to, how often they do things -- and that can be used in a negative
way. So there are privacy implications that come right to the forefront
as we begin to identify folks.

That that will be an
interesting discussion going forward, as the data comes out, patterns
start to unfold, patterns become uniquely identifiable to cities,
buildings, and individuals. What do we do with those unintended
consequences?

There are always situations where any new technology or any new capability could ultimately be used in a negative fashion.

It's
almost going to be sort of a two-step, where we can make a couple of
steps forward in progress and technology, then we are going to have to
deal with these issues, and it might take us a step back. It's
definitely evolving in this area, and these unintended consequences
could be very detrimental if not addressed early.

We
don’t want to completely shut down these types of activities based on
privacy concerns or some other type of legalities, when we could
actually potentially solve for those problems in a systematic
perspective, as we move forward with the investigation of the usage of
those technologies.

Muller: The
question we always need to bear in mind here is, as Brett talks about
it, what are the potential unintended consequences? How can we get in
front of those potential misuses early? How can we be vigilant of those
misuses and put in place good governance ahead of time?

There
are three approaches. One is to bury your head in the send and pretend
it will never happen. Second is to avoid adopting a technology at all
for fear of those unintended consequences. The third is to be aware of
them and be constantly looking for breaches of policy, breaches of good
governance, and being able to then correct for those if and when they do
occur.

Closed-loop cycle

Gardner: What is HP is doing that will set the stage and perhaps
help others to learn how to get started in terms of better security and
better leveraging of big data as a tool for better security?

Wahlin:
As HP progresses into the predicted security front, we're one of, I
believe, two companies that are actually trying to understand how to
best use HAVEn as we begin the analytics to determine the appropriate
usage of the data that is at our fingertips. That takes a predictive
capability that HP will be building.

The lagging piece of this would be the actual creation of agile security.

We've
created something called the Cyber Intelligence Center. The whole
intent of that is to develop the methodologies around how the big data
is used, the plumbing, and then the sources for which we actually create
the big data and how we move logs into big data. That's very different
than what we're doing today, traditional ArcSight loggers and ESMs.
There are a lot of mechanics that we have to build for that.

Then,
as we move out of that, we begin to look at the actual actionable
intelligence creation to use the analytics. What questions should we
ask? Then, when we get the answer, is it something we need to do
something about? The lagging piece of this would be the actual creation
of agile security. In some places, we even call it mobile security, and
it's different than mobility. It's security that can actually move.

If
you look at the war-type of analogies, back in the day, you had these
columns of men with rifles, and they weren’t that mobile. Then, as you
got into mechanized infantry and other types of technologies came
online, airplanes and such, it became much more mobile. What's the
equivalent to that in the cyber security world, and how do we create
that.

Right now, it's quite difficult to move a firewall around. You don’t just unplug or re-VLAN
a network. It's very difficult. You bring down applications. So what is
the impact of understanding what's coming at you, maybe tomorrow, maybe
next week? Can we actually make a infrastructure such that it can be
reconfigured to not only to defend against that attack, but perhaps even
introduce some adversarial confusion.

I've done my
reconnaissance. It looks like this. I come at it tomorrow, and it looks
completely different. That is the kill chain that will set back the
adversary quite a bit, because most of the time, during a kill chain,
it's actually trying to figure out where am I, what I have, where the
are assets located, and doing reconnaissance through the network.

So
there are a lot of interesting things that we can do as we come to this
next step in the evolution of security. At HP, we're trying to develop
that at scale. Being the large company that we are, we get the
opportunity to see an enormous amount of data that we wouldn’t see if we
are another company.

Numerous networks

Gardner: Paul, it
almost sounds as if security is an accelerant to becoming a better
organization, a more data-driven organization which will pay dividends
in many ways.

Muller: I
completely agree with you. Information security and the arms race, quite
literally the analogy, is a forcing function for many organizations. It
would be hard to say this without a sense of chagrin, but the great
part about this is that there are actually technologies that are being
developed as a result of this. Take ArcSight Logo as an example, as a
result of this arms race.

Just as the space race threw up a whole bunch of technologies like
Teflon or silicon adhesives that we use today, the the security arms
race is generating some great byproducts.

Those
technologies can now be applied to business problems, gathering
real-time operational technology data, such as seismic events, Twitter
feeds, and so forth, and being able to incorporate those back in for
business and public-good purposes. Just as the space race threw up a
whole bunch of technologies like Teflon or silicon adhesives that we use
today, the the security arms race is generating some great byproducts
that are being used by enterprises to create value, and that’s a
positive thing.

Wahlin: The analogy of the space race is perfect, as you look at
trying to do the security maturation within an environment. You begin to
see that a lot of the things that we're doing, whether it's
understanding the environment, being able to create the operational
metrics around an environment, or push into the fact that we've got to
get in front of the adversaries to create the environment that is
extremely agile is going to throw off a lot of technology innovations.

It’s
going to throw off some challenges to the IT industry and how things
are put together. That’s going to force typically sloppy operations --
such as I am just going to throw this up together, I am not going to
complete an acquisition, I don’t document, I don't understand my
environmental -- to clean it up as we go through those processes.

The
confusion and the complexity within an environment is directly opposed
to creating a sense of security. As we create the more secure
environment, environments that are capable of detecting anomalies within
them, you have to put the hygienic pieces in place. You have to create
the technologies that will allow you to leapfrog the adversaries. That’s
definitely going to be both a driver for business efficiencies, as well
as technology, and innovation as it comes down.